Advancing Banking Analytics for Enhanced Customer Insights

For years, financial institutions have struggled with inefficiencies from information and operational silos. Specialized processes and ingrained practices compound the difficulty. This fragmentation scatters customer insights, demanding significant resources to gather.

To overcome this obstacle, collaboration between business and data teams is crucial. Together, they can develop a comprehensive list of functional and non-functional requirements for a custom AI solution.

Our client — a Fortune 50 multinational retail bank with over 200 million customers — found that silos and fragmented data practices led to poor customer experiences and team performance.Customers engaged with the brand via multiple channels and touchpoints, with data on these encounters captured by the client. Yet, this information was dispersed among different departments, impeding the creation of unified customer profiles and the use of customer intelligence to tailor products and services to individual preferences.

Accelerating customer insight acquisition

The growing need for customer insights puts immense pressure on our client’s in-house analytics team. The team found it increasingly difficult to keep pace as demand surged, primarily due to a global talent shortage. Achieving the necessary speed and scale in delivering customer insights emerged as a challenge, leading to a gap between the business and data teams. This gap resulted in operational inefficiencies and increased costs due to delays in furnishing detailed customer insights.

Bridging the customer engagement gap in finance

Our client adopted a rule-based approach to customer engagement to navigate the delays in obtaining customer insights. The shift was intended to counter the growing demand for customer intelligence. However, this method proved inadequate for addressing each customer’s unique needs, stimulating the need for an advanced decision-making strategic solution.

To solve the client’s problem, we created individualized customer profiles reflecting the full view of each customer’s relationship with the brand. The information was then shared across business departments to facilitate easy information sharing. Instead of multiple incomplete customer profiles, business, and data teams now had access to the same comprehensive profile. Each profile could be updated after every interaction, regardless of the team involved.

What we provided:

AI-driven analytics and communication

Introducing a self-serve analytical bench transformed the workload distribution within the company. It empowered the in-house analytics team to concentrate on intricate customer decision-making tasks by delegating routine analyses to AI-driven systems accessible to business teams. Automating customer communications for basic inquiries also freed up time for the sales and business teams to focus on more meaningful customer interactions and conversions.

The enterprise’s newly deployed solution was optimized to serve more than 10 unique use cases, up from three in the original MVP solution. The new Customer 360 platform offers the enterprise a consolidated and individualized view of all crucial customer data. This includes demographics, product holding, transactions, and digital and non-digital interactions.